import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import argparse

#Network from https://github.com/floydhub/mnist/blob/master/main.py
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    #mode  0 ===> one hot encoded output
    #mode  1 ===> see below
    #mode  2 ===> see below
    #mode>=3 ===> softmax output
    def forward(self, x, mode=0):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        if mode==1:
            return x
        x = F.relu(self.fc1(x))
        if mode==2:
            return x
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        x = F.log_softmax(x, dim=-1)
        return x
